Scale dependence of landscape-structure-based estimation of abundance of Eurasian skylark (Alauda arvensis)

•-Identify the Eurasian skylark habitat and nonhabitat types identified.•-Eurasian skylark’s landscape-composition preferences determined.•- Impact of the scale of land cover dataset in population estimation.•-Impact of data granularity (buffer zones) in population estimation assessed. The habitat and occurrence of farmland birds are strongly determined by the agricultural-landscape structure. Changes in land cover composition and configuration are one of the main causes of the significant decline in abundance of the Eurasian skylark (Alauda arvensis) in recent decades. This farmland-bird species is common in agricultural areas of Eurasia. In this study we investigate the land use factors involved in the decline in Central Europe, Hungary. We used two different land use/land cover (LULC) datasets, which were compiled at different scales: the Ecosystem Map of Hungary (EMH), a very precise LULC map based on a 0.04-ha minimum mapping unit, and the Corine Land cover (CLC) dataset, built using a 25-ha minimum mapping unit. We studied the impact of landscape composition and configuration on skylark abundance by using negative-binomial generalized linear models. After identifying skylark preferences among LULC categories at different scales, we calculated the EMH and CLC dataset-based landscape indices (such as mean patch size and mean fractal dimension index) of the skylark preferred (arable lands, pastures, grasslands and meadows) and the nonpreferred (artificial surfaces, forests, complex cultivation patterns and waters) LULC classes. Then we compared the results with field observations of skylark abundance in the database of Hungarian Common Bird Monitoring (MMM). On the basis of statistical analysis of connections between the landscape indices and the skylark-abundance data, we estimated skylark abundance for those areas where the skylark-abundance datasets from field observation were not available. We also tested the estimates by assessing model sensitivity when we input different-scale LULC data and used different observation windows (grain size). Our statistical model using EMH dataset explained 41.22% variance of the skylark abundance data, while the rest, 58.78% of variance is not accounted by the model presumably due to local environmental factors not considered in our model. The regional scale (CLC-based) estimation of skylark abundance yielded significantly lower accuracies (33.76% in 1200 m radius buffer zones and 34.11% in 600 m radius buffer zones